A magnetic resonance phase contrast film imaging method, device, product and medium

Abstract

The present invention discloses a magnetic resonance phase contrast film imaging method, equipment, product and medium, the method comprising: based on the signal acquisition of the target object based on the phase contrast sequence, a plurality of spoke data are obtained in each channel of each coil; based on allocating each spoke data in each channel to the corresponding sub-k space, the first k space of each channel is obtained; based on the first k space and coil sensitivity information, the first reconstructed image set is obtained; based on the phase calculation of the first reconstructed image set, the first flow velocity phase map and the second flow velocity phase map are obtained; based on the first flow velocity phase map, the second flow velocity phase map and the mask map, the phase unwrapping is performed to obtain the third flow velocity phase map; based on the third flow velocity phase map and the mask map, the phase distribution is modified to obtain the human tissue information map. The present invention can take into account the flow velocity measurement of slow flow velocity cavities and fast flow velocity cavities; the introduction of the sparsity of the flow velocity encoding dimension can achieve a higher acceleration multiple.

Description

Technical Field

[0001] The present invention relates to the field of medical imaging technology, and in particular to a magnetic resonance phase contrast film imaging method, equipment, product and medium. Background Art

[0002] Magnetic resonance imaging (MRI) is a technique that uses rapid MRI imaging sequences to image moving organs, providing information about movement and function during the cardiac cycle. However, the slow imaging speed of MRI MRI may lead to artifacts.

[0003] Magnetic resonance phase contrast cine imaging uses flow encoding gradients to perform phase contrast imaging of fluids and surrounding static tissues, thereby showing the flow of the fluid. This technology is suitable for visualizing the flow of fluids, such as blood flow, cerebrospinal fluid flow, or other body fluid flow. Because magnetic resonance phase contrast cine imaging technology can provide rich physiological information including vascular status, flow velocity and direction in blood vessels during the ECG cycle, it is increasingly valued by cardiovascular disease research. However, traditional magnetic resonance phase contrast cine imaging technology has a long acquisition time and faces the challenge of respiratory movement when imaging parts such as the heart and abdomen.

[0004] There is a high-acceleration real-time phase contrast magnetic resonance imaging technology that can generate two-dimensional images containing multiple ECG cycles in a short time under free breathing acquisition mode, and can achieve a high scanning success rate and scanning efficiency. However, this magnetic resonance imaging technology is not robust enough for measuring the flow velocity of some vascular beds with large differences in flow velocity between large and small blood vessels within the imaging range; and the constraints considered in the reconstruction method used are very limited, and it is impossible to further improve the acquisition ratio of magnetic resonance data.

[0005] Therefore, in view of the above shortcomings, it is urgent to propose a new magnetic resonance phase contrast movie imaging technology. Summary of the invention

[0006] In a first aspect, an embodiment of the present invention provides a magnetic resonance phase contrast cine imaging method, comprising:

[0007] Providing a magnetic resonance system comprising C coils;

[0008] Provides phase contrast sequences combining golden angle radial acquisition and velocity encoding;

[0009] The C coils collect signals of C channels from the target object based on the phase contrast sequence, and N spoke data are collected in each channel of each coil; wherein each spoke data includes physiological information, golden angle information, V flow velocity encoding information and acquisition time information;

[0010] Based on allocating N spoke data in each channel to P*V sub-k-spaces based on P tissue state encodings and V flow velocity encoding information, a first k-space of each channel is obtained; wherein the first k-space includes P*V sub-k-spaces;

[0011] Based on the first k-space and coil sensitivity information, a compressed sensing method is used to reconstruct to obtain a first reconstructed image set of P*V first reconstructed sub-images based on tissue state coding and flow velocity coding information;

[0012] A first flow velocity phase map and a second flow velocity phase map based on each tissue state encoding are obtained based on the phase calculation performed on the first reconstructed image set; wherein the flow velocity corresponding to the first flow velocity phase map is less than the flow velocity corresponding to the second flow velocity phase map;

[0013] Providing a mask image, the mask image is used to distinguish biological tissue from air;

[0014] Performing phase unwrapping based on the first flow velocity phase map, the second flow velocity phase map and the mask map to obtain a third flow velocity phase map encoded based on each tissue state;

[0015] The phase distribution modification is performed based on the third flow velocity phase image and the mask image to obtain a human tissue information image.

[0016] In a second aspect, an embodiment of the present invention provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the magnetic resonance phase contrast movie imaging method as described above when executing the computer program.

[0017] In a third aspect, an embodiment of the present invention provides a computer program product, comprising a program executed by at least one processor of a computing device, the program comprising instructions, wherein execution of the instructions enables the at least one processor to perform the magnetic resonance phase contrast movie imaging method as described above.

[0018] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to perform the magnetic resonance phase contrast movie imaging method as described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic flow chart of a magnetic resonance phase contrast cine imaging method according to an embodiment of the present invention;

[0020] Figure 2 Schematic diagram of golden angle radial acquisition trajectory of an embodiment of the present invention;

[0021] Figure 3 Schematic diagram of the principle of realizing flow imaging by using velocity encoding gradient in an embodiment of the present invention;

[0022] Figure 4 It is a schematic diagram of data collection performed by a flow rate encoding and golden angle combined circulation method according to an embodiment of the present invention;

[0023] Figure 5 is a schematic diagram of a first k-space according to an embodiment of the present invention;

[0024] Figure 6 is a schematic structural diagram of a magnetic resonance system according to an embodiment of the present invention;

[0025] Figure 7 is a schematic diagram of the structure of a terminal device according to an embodiment of the present invention;

[0026] in, Figure 6 In the figure, 1. magnetic resonance system; 2. examination area; 3. receiving coil; 4. bed; 6. gradient coil; 7. radio frequency coil; P. target object; 10. magnet; 11. magnetic resonance controller; 12. gradient controller; 13. radio frequency controller; 14. recording unit; 15. memory; 16. computing unit; 17. input unit; 18. display. DETAILED DESCRIPTION

[0027] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0028] like Figure 1 As shown, an embodiment of the present invention provides a magnetic resonance phase contrast movie imaging method, comprising the steps of:

[0029] S1. providing a magnetic resonance system comprising C coils;

[0030] One coil represents one channel, and the magnetic resonance system includes C coils, ie, C channels.

[0031] S2, providing phase contrast sequences combining golden angle radial acquisition and velocity encoding;

[0032] Golden angle radial acquisition is an acquisition trajectory for free breathing imaging. It has the advantage of being insensitive to motion, can extract self-navigation signals, and classify periodic motion states. Moreover, due to the distribution of its golden angle, it can be well combined with compressed sensing reconstruction methods to retrospectively reconstruct imaging of arbitrary time resolution.

[0033] like Figure 2As shown in the figure, it is a schematic diagram of the one-dimensional version of the golden angle radial acquisition trajectory. Among them, lines 1 to 4 represent k-space lines, each of which passes through the center of k-space. Such k-space lines are also called spokes; kx and ky represent two-dimensional coordinates in k-space, corresponding to the spatial frequency information in the horizontal and vertical directions of the image respectively; Represents the angle between two adjacent collections, also known as the golden angle.

[0034] Golden angle radial acquisition methods include stacked star, radial-2D, and radial-3D. For the stacked star acquisition method, the FOV (field of view) is cylindrical or elliptical; for the radial-2D acquisition method, the FOV is circular or elliptical; for the radial-3D acquisition method, the FOV is spherical or ellipsoidal. For the elliptical, elliptical, and ellipsoidal FOVs, the spacing angle between the two spokes needs to be calculated separately according to the FOV in each direction.

[0035] Velocity encoding can be used to create a phase difference between a stationary object and a moving object, thereby achieving imaging of the visualized fluid. Figure 3 As shown in the figure, it is a schematic diagram of the principle of flow imaging using velocity encoding gradient. Among them, the bipolar gradient is the velocity encoding gradient. Since the zero-order moment of this pair of gradients is 0, the phase change of the stationary object after applying this pair of gradients is 0; the first-order moment is not 0, and the moving object has a phase change after experiencing this pair of gradients, thereby producing a phase difference with the phase of the stationary object. The value of the first-order moment can be modified by changing the size of the gradient and the position of the center of gravity. The inverse of the first-order moment is called the velocity encoding value (velocity encoding, venc), and the changed phase value is equal to the velocity multiplied by the velocity encoding value. Due to the existence of the background field, it is also necessary to collect a set of velocity compensation data, whose zero-order moment and first-order moment of the bipolar gradient are both 0.

[0036] Specifically, the flow velocity coding includes several flow coding directions, and each flow coding direction includes venc values ​​corresponding to two different flow velocities. Furthermore, the flow velocity coding also includes a full flow velocity compensation code. Among them, two venc values ​​corresponding to different flow velocities are set in each flow coding direction in order to comprehensively consider the acquisition efficiency and the effect of phase unwrapping. One venc value corresponds to the upper limit of the body fluid flow velocity in the coarse cavity of the target object, and the other venc value corresponds to the upper limit of the body fluid flow velocity in the fine cavity of the target object. It can be understood that the former is greater than the latter. The flow velocity coding includes three flow coding directions. In this embodiment, the venc value corresponding to the upper limit of the body fluid flow velocity in the coarse cavity of the target object is recorded as the first flow velocity coding value, and the venc value corresponding to the upper limit of the body fluid flow velocity in the fine cavity of the target object is recorded as the second flow velocity coding value.

[0037] Furthermore, considering that the change of flow velocity in the cavity is relatively smooth in the spatial dimension, the image of high flow velocity coding value with low spatial resolution can be used to assist the deconvolution of the image of low flow velocity coding value with high spatial resolution. Therefore, in this embodiment, the acquisition ratio of the second flow velocity coding value in the same flow coding direction is set to twice the acquisition ratio of the first flow velocity coding value.

[0038] S3, C coils collect signals of C channels from the target object based on the phase contrast sequence, and N spoke data are collected in each channel of each coil;

[0039] Specifically, C coils collect signals of C channels of the target object in a combined cycle of flow velocity encoding and golden angle based on the phase contrast sequence, and N spoke data are collected in each channel of each coil, that is, a total of N*C spoke data are collected. Each spoke data includes physiological information, golden angle information, V flow velocity encoding information and acquisition time information.

[0040] The cyclic mode of the phase contrast sequence is the combined cyclic acquisition of flow velocity encoding information and golden angle information. This embodiment is set up to include a full flow compensation (Flow Compensation, FC), RL (Right-Left, left and right direction), AP (Anterior-Posterior, front and back direction) and FH (Foot-Head, foot-head direction) three flow encoding directions, each with two venc values, to obtain a total of seven flow velocity encoding information. Specifically, these seven flow velocity encoding information are respectively expressed as follows:

[0041] [VENC FC ],[VENC RL,LOW ],[VENC RL,HIGH ],[VENC AP,LOW ],[VENC AP,HIGH ],[VENC FH,LOW ],[VENC FH,HIGH ].

[0042] Furthermore, set [VENC RL,LOW ]、[VENC AP,LOW ] and [VENC FH,LOW ] are all collected at 20%, set [VENC FC ]、[VENC RL,HIGH ]、[VENC AP,HIGH ] and [VENC FH,HIGH ] The collection ratio is 10%.

[0043] Furthermore, if Figure 4As shown in the figure, it is a schematic diagram of data acquisition using a combined circulation method of velocity coding and golden angle. The golden angle is obtained by means of radial-3D acquisition. The azimuth angle [theta, phi] of each azimuth read out can be expressed as [mod(gratio2D[1]*i*pi, 2pi), mod(gratio2D[2]*i*pi, 2pi)]. Among them, gratio2D is the two-dimensional golden ratio, that is, [0.4656, 0.6823]. The combined circulation method of velocity coding and golden angle is expressed as follows:

[0044] [VENC FC ,mod(gratio2D[1]*pi,2pi),mod(gratio2D[2]*pi,2pi)],

[0045] [VENC RL,LOW ,mod(gratio2D[1]*2*pi,2pi),mod(gratio2D[2]*2*pi,2pi)],

[0046] [VENC RL,HIGH ,mod(gratio2D[1]*3*pi,2pi),mod(gratio2D[2]*3*pi,2pi)],

[0047] [VENC AP,LOW ,mod(gratio2D[1]*4*pi,2pi),mod(gratio2D[2]*4*pi,2pi)],

[0048] [VENC AP,HIGH ,mod(gratio2D[1]*5*pi,2pi),mod(gratio2D[2]*5*pi,2pi)],

[0049] [VENC FH,LOW ,mod(gratio2D[1]*6*pi,2pi),mod(gratio2D[2]*6*pi,2pi)],

[0050] [VENC FH,HIGH ,mod(gratio2D[1]*7*pi,2pi),mod(gratio2D[2]*7*pi,2pi)],

[0051] [VENC RL,LOW ,mod(gratio2D[1]*8*pi,2pi),mod(gratio2D[2]*8*pi,2pi)],

[0052] [VENC AP,LOW ,mod(gratio2D[1]*9*pi,2pi),mod(gratio2D[2]*9*pi,2pi)],

[0053] [VENC FH,LOW ,mod(gratio2D[1]*10*pi,2pi),mod(gratio2D[2]*10*pi,2pi)],

[0054] [VENC FC ,mod(gratio2D[1]*11*pi,2pi),mod(gratio2D[2]*11*pi,2pi)],...

[0055] Furthermore, the golden angle is obtained by radial-2D acquisition, and its combined circulation method is similar to the combined circulation method corresponding to radial-3D mentioned above, except that its azimuth angle uses the one-dimensional golden ratio gratio1D, and its value is 0.618. Similarly, its velocity encoding and golden angle combined circulation method are expressed as follows:

[0056] [VENC FC ,mod(gratio1D*pi,2pi)],

[0057] [VENC RL,LOW ,mod(gratio1D*2*pi,2pi)],

[0058] [VENC RL,HIGH ,mod(gratio1D*3*pi,2pi)],

[0059] [VENC AP,LOW ,mod(gratio1D*4*pi,2pi)],

[0060] [VENC AP,HIGH ,mod(gratio1D*5*pi,2pi)],

[0061] [VENC FH,LOW ,mod(gratio1D*6*pi,2pi)],

[0062] [VENC FH,HIGH ,mod(gratio1D*7*pi,2pi)],

[0063] [VENC RL,LOW ,mod(gratio1D*8*pi,2pi)],

[0064] [VENC AP,LOW ,mod(gratio1D*9*pi,2pi)],

[0065] [VENC FH,LOW ,mod(gratio1D*10*pi,2pi)],

[0066] [VENC FC ,mod(gratio1D*11*pi,2pi)],...

[0067] Furthermore, the golden angle is obtained by means of the stacked star acquisition method, and its joint circulation method is similar to the joint circulation method corresponding to the above radial-2D. Since the stacked star has an additional dimension of layer-wise phase encoding in the z direction for Cartesian encoding compared to radial-2D, all flow velocity encodings in the kz direction can be circulated first, and then the golden angle can be circulated. Alternatively, in order to achieve a higher level of sparsity, all flow velocity encodings and golden angles in the kz direction are circulated in an interlaced manner.

[0068] S4, obtaining the first k-space of each channel by allocating N spoke data in each channel to P*V sub-k-spaces based on P tissue state codes and V flow velocity code information;

[0069] Specifically, P organization status codes are obtained, including:

[0070] The same spoke data based on the phase contrast sequence in different channels are merged to obtain a merged spoke, the low-frequency part of the merged spoke is extracted, and according to the acquisition time of the signal acquisition, a signal strength sequence based on the acquisition time is obtained, the signal strength sequence is filtered to obtain a filtered signal, and P tissue state codes are selected based on the filtered signal. Among them, the arithmetic square root of the sum of squares can be used to merge the same spoke data in different channels. The filtered signal includes physiological signals of different frequency bands, including at least an electrocardiogram signal and a respiratory signal. The frequency band of the electrocardiogram signal is between 0.5 and 2 Hz, and the frequency band of the respiratory signal is between 0.2 and 0.3 Hz. Since the order of acquisition is the golden angle, the signal strength sequence has a strong component at the frequency corresponding to the golden ratio. When designing the sequence parameters, it is necessary to avoid the overlap of the frequency band with the frequency of the electrocardiogram signal. It should be noted that the P value is customized. The larger the P value, the less blurry the image is, but the signal-to-noise ratio will be lower. In this embodiment, the value range of the P value is set to [10,15], and the tissue state code is the electrocardiogram state code. In addition, tissue state encoding can be customized based on methods such as ECG cycle, TR and view sharing.

[0071] Furthermore, each spoke data in each channel is assigned to its own sub-k-space according to the physiological information and velocity encoding information corresponding to its sampling, and a set of P×V k-space is formed in each channel, namely the first k-space, which can be seen in Figure 5 Wherein, P represents the number of tissue state codes, and V represents the number of flow rate code information. The first k-space includes P*V sub-k-spaces, each of which corresponds to different tissue state codes and flow rate code information.

[0072] It should be noted that in order to obtain an image that is not affected by respiratory motion, the respiratory signal obtained by filtering can be used as a gating signal. Specifically, selecting the spoke data collected at the end of exhalation can reconstruct a high-resolution image without blurring caused by motion, and can also be used as a basis for classifying multiple respiratory states and performing multi-dimensional reconstruction.

[0073] S5. Reconstructing by using a compressed sensing method based on the first k-space and coil sensitivity information to obtain a first reconstructed image set of P*V first reconstructed sub-images based on tissue state coding and flow velocity coding information;

[0074] It should be noted that the coil sensitivity information and tissue state coding are independent of the flow velocity coding information, that is, the coil sensitivity information under different tissue state coding and flow velocity coding information is unchanged.

[0075] Specifically, the coil sensitivity information is obtained, including:

[0076] Based on the second reconstructed image obtained by reconstructing the second k-space composed of multiple sub-k-spaces, the coil sensitivity information of the magnetic resonance system is obtained. The second k-space is composed of a number of continuously sampled sub-k-spaces, and the second k-space meets the full sampling condition. Inverse Fourier transform is used in the process of obtaining the second reconstructed image based on the second k-space reconstruction. The coil sensitivity information is a matrix of matrixsize*matrixsize*C, that is, it includes the sensitivity of each coil.

[0077] The signal of C channels of the target object is collected by radial-2D or stacked star mode. The full sampling condition is that the number of points of each spoke is matrixsize*2, and the number of spokes in the second k-space is greater than matrixsize*pi / 2. Among them, matrixsize represents the matrix size of the first k-space, and pi represents the pi.

[0078] The radial-3D method is used to collect signals of C channels of the target object. The full sampling condition is that the number of points in each spoke is matrixsize*2, and the number of spokes in the second k-space is greater than matrixsize*matrixsize*pi*pi / 4.

[0079] Further, based on the first k-space and coil sensitivity information, a compressed sensing method is used to reconstruct, and a first reconstructed image set of P*V first reconstructed sub-images based on tissue state coding and flow velocity coding information is obtained, including:

[0080] Based on a first operation item including the first k-space, a second operation item including the reconstructed image set of all tissue state codes corresponding to each flow velocity coding information, a third operation item including the reconstructed image set of all flow velocity coding information corresponding to each tissue state coding, and coil sensitivity information, a compressed sensing method is used to reconstruct and obtain a first reconstructed image set of P*V first reconstructed sub-images based on tissue state coding and flow velocity coding information.

[0081] Specifically, a non-uniform Fourier transform is used to perform data fidelity to obtain a first operation term including the first k-space, that is, the first operation term is an original data fidelity term. In this embodiment, the first operation term is represented by the following formula:

[0082]

[0083] Where F represents the non-uniform Fourier transform operator, S represents the coil sensitivity information, represents the image corresponding to the pth tissue state code and the vth flow velocity code information, Represents the sub-k-space corresponding to the p-th tissue state encoding and the v-th flow velocity encoding information.

[0084] The second operation term is a sparse term of the tissue state encoding dimension. In this embodiment, the second operation term is represented by the following formula:

[0085]

[0086] in, represents the tissue state coded image sequence corresponding to the vth flow velocity coded information, and T represents an operator for calculating the difference of the sequence.

[0087] The third operation term is a sparse term of the velocity coding information dimension. In this embodiment, the third operation term is represented by the following formula:

[0088]

[0089] Among them, DIFF represents the pairwise difference operator, and abs represents the absolute value operator. Represents the flow rate encoding information image sequence corresponding to the p-th tissue state code.

[0090] Furthermore, based on minimizing the weighted sum of the first operation term, the second operation term and the third operation term, a cost function used for reconstruction using a compressed sensing method is obtained.

[0091] Specifically, this embodiment uses the following formula to express the cost function:

[0092]

[0093] in, represents the sparse coefficient of the tissue state encoding dimension, Sparse coefficients representing the dimension of velocity encoding information.

[0094] Furthermore, based on the iterative optimization of the cost function using the conjugate gradient method, a first reconstructed image set of P*V first reconstructed sub-images based on tissue state coding and flow velocity coding information is obtained. The initial value of It is obtained by non-uniform Fourier transform.

[0095] S6, performing phase calculation based on the first reconstructed image set to obtain a first flow velocity phase map and a second flow velocity phase map based on each tissue state encoding;

[0096] Specifically, the flow velocity coding includes several flow coding directions, and the phase calculation based on the first reconstructed image set is performed to obtain a first flow velocity phase map and a second flow velocity phase map based on each tissue state coding and each flow coding direction. Among them, the flow velocity coding includes three flow coding directions, and the flow velocity corresponding to the first flow velocity phase map is less than the flow velocity corresponding to the second flow velocity phase map.

[0097] Furthermore, the velocity encoding in this embodiment includes three flow encoding directions: RL, AP and FH. Specifically, the first velocity phase diagram of the RL flow encoding direction is calculated using the following formula:

[0098]

[0099] in, represents the first velocity phase diagram of the pth tissue state encoding in the RL flow encoding direction, Indicates the calculation symbol for the angle of a complex number. represents the first reconstructed sub-image corresponding to the p-th tissue state code and the low flow velocity code value in the RL flow code direction, Represents the flow-compensated image encoding the pth tissue state.

[0100] The second flow velocity phase diagram in the RL flow encoding direction is calculated using the following formula:

[0101]

[0102] in, The second velocity phase diagram representing the pth tissue state encoding in the RL flow encoding direction, Represents the first reconstructed sub-image corresponding to the p-th tissue state code and the high flow velocity code value in the RL flow code direction.

[0103] It should be noted that this embodiment performs phase calculation based on the first reconstructed image set to obtain the first flow velocity phase map and the second flow velocity phase map in the AP flow encoding direction, as well as the first flow velocity phase map and the second flow velocity phase map in the FH flow encoding direction. The calculation process can refer to the above formula and will not be repeated here.

[0104] S7, providing a mask image;

[0105] The mask image is used to distinguish biological tissue from air. It should be noted that the mask image can be provided based on a variety of sources. For example, the data collected by the coil is filled into the k-space, and then the k-space is inversely transformed by Fourier transform to obtain a magnetic resonance image. Based on the magnetic resonance image, a threshold for distinguishing biological tissue from air is set for screening.

[0106] Specifically, the process of providing the mask image in this step is: the second reconstructed image obtained based on the second k-space reconstruction is calculated based on a preset threshold, that is, binarized, to obtain a mask image that distinguishes biological tissue from air. Among them, the inverse Fourier transform is used in the process of obtaining the second reconstructed image based on the second k-space reconstruction. Furthermore, the preset threshold of this embodiment is 5% of the average value of the second reconstructed image, and pixels below the threshold are identified as air and represented by 0; pixels above the threshold are identified as biological tissue and represented by 1. The introduction of the mask image is conducive to improving the stability of subsequent phase unwrapping.

[0107] The mask image can be obtained based on a variety of methods. For example, in some embodiments, during a coil data acquisition process, the collected data is filled into the k-space, and then the k-space is inversely Fourier transformed to obtain a nuclear magnetic resonance image. Based on the obtained nuclear magnetic resonance image, screening is performed based on a threshold value for distinguishing tissue from air.

[0108] S8, performing phase unwrapping based on the first flow velocity phase map, the second flow velocity phase map and the mask map to obtain a third flow velocity phase map encoded based on each tissue state;

[0109] Specifically, the velocity encoding includes several flow encoding directions, and the phase unwrapping is performed based on the first velocity phase map, the second velocity phase map and the mask map to obtain a third velocity phase map based on each tissue state encoding and each flow encoding direction. The velocity encoding includes three flow encoding directions.

[0110] The third velocity phase image is the velocity phase image after phase unwrapping. Through phase unwrapping, a more accurate velocity phase image can be obtained. Moreover, the third velocity phase image of each flow encoding direction reflects the flow velocity of each flow encoding direction. These images can be used as input for analysis of flow velocity and flow rate.

[0111] S9. Modify the phase distribution based on the third flow velocity phase image and the mask image to obtain a human tissue information image.

[0112] The velocity coding includes several flow coding directions. Based on the phase distribution modification and calculation of the third velocity phase map and the mask map, the human tissue information map to be solved is obtained. Based on the components of each flow coding direction, the components of each flow coding direction are combined to obtain the human tissue information map. Among them, the velocity coding includes three flow coding directions.

[0113] Specifically, based on the phase distribution modification performed on the third flow velocity phase map and the mask map, a fourth flow velocity phase map based on each flow encoding direction is obtained. Among them, the fourth flow velocity phase map is the flow velocity phase map after the phase distribution modification. The flow velocity encoding in this embodiment includes three flow encoding directions: RL, AP, and FH. Specifically, the fourth flow velocity phase map of the RL flow encoding direction is calculated using the following formula:

[0114]

[0115] in, The fourth velocity phase diagram representing the RL flow encoding direction, The third velocity phase diagram showing the RL flow encoding direction, The mask image is shown. By modifying the phase distribution, the phase distribution reflecting the flow velocity is made more concentrated.

[0116] It should be noted that this embodiment performs phase distribution modification based on the third flow velocity phase map and the mask map to obtain the fourth flow velocity phase map in the AP flow encoding direction and the fourth flow velocity phase map in the FH flow encoding direction. The calculation process can refer to the above formula and will not be repeated here.

[0117] Further, based on the calculation of the fourth flow velocity phase map and the flow velocity compensation image, the components of the human tissue information map based on each flow encoding direction to be solved are obtained. Specifically, this embodiment uses the following formula to calculate the components of the human tissue information map based on the RL flow encoding direction:

[0118]

[0119]

[0120] in, represents the first reconstructed sub-image corresponding to the p-th tissue state code and RL flow code direction, represents a pure imaginary number, Represents the component of the human tissue information map based on the RL flow encoding direction.

[0121] It should be noted that this embodiment is based on the calculation of the fourth flow velocity phase map and the flow velocity compensation image to obtain the component of the human tissue information map based on the AP flow encoding direction, and the component of the human tissue information map based on the FH flow encoding direction. The calculation process can refer to the above formula and will not be repeated here.

[0122] Further, the components of each flow coding direction are combined to obtain a human tissue information map. The combination of components includes first calculating the sum of squares of all components and then calculating the arithmetic square root of the sum of squares. Specifically, the present embodiment uses the following formula to obtain the human tissue information map:

[0123]

[0124] in, Represents the human body organization infographic, The component representing the AP flow coding direction, The component representing the FH flow encoding direction. This calculation method can make human tissues such as small blood vessels display richer images, and large blood vessels will not be broken due to phase wrapping.

[0125] It should be noted that, in step S1, the magnetic resonance system further includes:

[0126] a magnetic resonance controller; and

[0127] A memory storing a program, the program comprising instructions, the magnetic resonance controller being connected to the memory and configured to execute the instructions, wherein the instructions, when executed by the magnetic resonance controller, cause the magnetic resonance system to perform the magnetic resonance phase contrast cine imaging method as described above.

[0128] refer to Figure 6, describes a magnetic resonance system 1, with which the above-mentioned magnetic resonance phase contrast cine imaging method can be performed. A target object P can be placed in an examination chamber in an examination region 2 of the magnetic resonance system 1. The magnetic resonance system 1 comprises a magnet 10 for generating a basic field B0. The target object P is moved into the examination region 2 at the center of the magnet 10, so that the magnetic resonance system 1 receives spatially encoded magnetic resonance signals from the examination region 2, which are acquired by at least one receiving coil 3 (C coils). By, for example, transmitting a radio frequency pulse sequence by the radio frequency coil 7 and switching the gradient magnetic field provided by the gradient coil 6, the nuclear spins in the examination region 2 can be deflected from the equilibrium position, and spin echoes and stimulated echo signals can be generated in the target object P. The stimulated echo signals and the currents caused by the spin echoes generated by the basic field B0 and returned to the equilibrium position can be converted into magnetic resonance signals in the receiving coils 3 for generating magnetic resonance measurement data. The magnetic resonance measurement signals detected by each receiving coil 3 are associated with the corresponding magnetic resonance measurement channel. The general operation mode for generating magnetic resonance images and detecting magnetic resonance signals is not described in detail here.

[0129] The magnetic resonance system 1 comprises a magnetic resonance controller 11 for controlling the magnetic resonance system 1. The magnetic resonance controller is suitable for executing a magnetic resonance phase contrast cine imaging method of the present invention, and the magnetic resonance controller 11 also comprises a gradient controller 12 and a radio frequency controller 13, wherein the gradient controller 12 is used to control and switch the gradient magnetic field, and the radio frequency controller 13 is used to control and transmit radio frequency pulses for deflecting nuclear spins from the equilibrium position. In the memory 15, for example, the imaging sequence or pulse sequence described in recording and acquiring magnetic resonance images, as well as the program described in the operation of the magnetic resonance system 1 can be stored. The recording unit 14 controls the image recording, thereby controlling the sequence of gradient magnetic fields and radio frequencies and the receiving interval of magnetic resonance measurement signals as a function of the determined imaging sequence. The recording unit 14 also controls the gradient controller 12 and the radio frequency controller 13. The magnetic resonance image that can be displayed on the display 18 can be calculated in the calculation unit 16, wherein the operator operates the magnetic resonance system 1 via the input unit 17. The memory 15 may have an imaging sequence (or pulse sequence) and a program module. When instructions included in one of the program modules are executed in the computing unit 16, the imaging sequence and the instructions included in the program execute a magnetic resonance phase contrast cine imaging method involved in the present disclosure.

[0130] The embodiment of the present invention is a magnetic resonance phase contrast movie imaging method, which proposes a phase contrast sequence of unidirectional dual flow velocity encoding combined with golden angle radial acquisition. The data acquisition method of the combined cycle of flow velocity encoding and golden angle can provide a better sub-k space distribution, which is conducive to constrained reconstruction in combination with compressed sensing; the flow velocity encoding with adjustable acquisition ratio allows the sequence to take into account the flow velocity measurement of the slow flow cavity and the fast flow cavity at the same time, saving the total acquisition time as much as possible; the consistency of the flow velocity encoding dimension can further improve the sparsity and achieve a higher acceleration factor; the phase distribution modification after phase unwrapping can compress the phase distribution representing the flow velocity, and the slow flow cavity obtained after the complex difference is more richly displayed, and the fast flow cavity will not be discontinuous.

[0131] An embodiment of the present invention further provides a terminal device, the terminal device comprising:

[0132] processor, memory, and bus;

[0133] The bus is used to connect the processor and the memory;

[0134] The memory is used to store operation instructions;

[0135] The processor is used to call the operation instruction, and the executable instruction enables the processor to perform the operation corresponding to the magnetic resonance phase contrast movie imaging method as described above in the present invention.

[0136] In an optional embodiment, a terminal device is provided, such as Figure 7 As shown, Figure 7 The terminal device 5000 shown includes: a processor 5001 and a memory 5003. The processor 5001 and the memory 5003 are connected, such as through a bus 5002. Optionally, the terminal device 5000 may also include a transceiver 5004. It should be noted that in actual applications, the transceiver 5004 is not limited to one, and the structure of the terminal device 5000 does not constitute a limitation on the embodiments of the present invention.

[0137] Processor 5001 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. Processor 5001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0138] The bus 5002 may include a path to transmit information between the above components. The bus 5002 may be a PCI bus or an EISA bus, etc. The bus 5002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0139] The memory 5003 may be a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, or an EEPROM, a CD-ROM or other optical disk storage, an optical disk storage (including a compressed optical disk, a laser disk, an optical disk, a digital versatile disk, a Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0140] The memory 5003 is used to store application code for executing the solution of the present invention, and the execution is controlled by the processor 5001. The processor 5001 is used to execute the application code stored in the memory 5003 to implement the content shown in any of the above method embodiments.

[0141] Among them, terminal devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc.

[0142] The embodiment of this aspect also provides a computer program product, including a program executed by at least one processor of a computing device, the program including instructions, wherein the execution of the instructions enables the at least one processor to perform a magnetic resonance phase contrast movie imaging method according to the aforementioned method.

[0143] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the magnetic resonance phase contrast movie imaging method described above is implemented.

[0144] Yet another embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed on a computer, enables the computer to execute the corresponding contents of the aforementioned method embodiments.

[0145] In addition, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented.

[0146] In summary, the embodiments of the present invention provide a magnetic resonance phase contrast movie imaging method, device, product and medium, and propose a phase contrast sequence of unidirectional dual flow velocity encoding combined with golden angle radial acquisition. The data acquisition method of the combined cycle of flow velocity encoding and golden angle can provide a better sub-k space distribution, which is conducive to constrained reconstruction in combination with compressed sensing; the flow velocity encoding with adjustable acquisition ratio allows the sequence to take into account the flow velocity measurement of the slow flow cavity and the fast flow cavity at the same time, saving the total acquisition time as much as possible; the consistency of the flow velocity encoding dimension can further improve the sparsity and achieve a higher acceleration factor; the phase distribution modification after phase unwrapping can compress the phase distribution representing the flow velocity, and the slow flow cavity obtained after the complex difference is displayed more richly, and the fast flow cavity will not be discontinuous.

[0147] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referenced to each other, and each embodiment focuses on the differences from other embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0148] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention. These improvements and substitutions should also be regarded as the scope of protection of the present invention.

Claims

1. A magnetic resonance phase contrast cine imaging method, characterized in that: include: Providing a magnetic resonance system comprising C coils; Provides phase contrast sequences combining golden angle radial acquisition and velocity encoding; The C coils collect signals of C channels from the target object based on the phase contrast sequence, and N spoke data are collected in each channel of each coil; wherein each spoke data includes physiological information, golden angle information, V flow velocity encoding information and acquisition time information; Based on allocating N spoke data in each channel to P*V sub-k-spaces based on P tissue state encodings and V flow velocity encoding information, a first k-space of each channel is obtained; wherein the first k-space includes P*V sub-k-spaces; Based on the first k-space and coil sensitivity information, a compressed sensing method is used to reconstruct to obtain a first reconstructed image set of P*V first reconstructed sub-images based on tissue state coding and flow velocity coding information; A first flow velocity phase map and a second flow velocity phase map based on each tissue state encoding are obtained based on the phase calculation performed on the first reconstructed image set; wherein the flow velocity corresponding to the first flow velocity phase map is less than the flow velocity corresponding to the second flow velocity phase map; Providing a mask image, wherein the mask image is used to distinguish between biological tissue and air; Performing phase unwrapping based on the first flow velocity phase map, the second flow velocity phase map and the mask map to obtain a third flow velocity phase map encoded based on each tissue state; The phase distribution modification is performed based on the third flow velocity phase image and the mask image to obtain a human tissue information image.

2. The magnetic resonance phase contrast cine imaging method according to claim 1, characterized in that: The flow velocity coding includes a plurality of flow coding directions, and each flow coding direction includes venc values ​​corresponding to two different flow velocities.

3. The magnetic resonance phase contrast cine imaging method according to claim 2, characterized in that: The flow rate encoding also includes a full flow rate compensation encoding.

4. The magnetic resonance phase contrast cine imaging method according to claim 2, characterized in that: One venc value corresponds to an upper limit of the flow rate of the body fluid in the coarse cavity of the target object, and the other venc value corresponds to an upper limit of the flow rate of the body fluid in the fine cavity of the target object.

5. The magnetic resonance phase contrast cine imaging method according to claim 1, characterized in that: The flow velocity encoding includes several flow encoding directions, and a first flow velocity phase map and a second flow velocity phase map based on each tissue state encoding and each flow encoding direction are obtained based on the phase calculation performed on the first reconstructed image set.

6. The magnetic resonance phase contrast cine imaging method according to claim 1, characterized in that: The flow velocity encoding includes several flow encoding directions. Phase unwrapping is performed based on the first flow velocity phase map, the second flow velocity phase map and the mask map to obtain a third flow velocity phase map based on each tissue state encoding and each flow encoding direction.

7. The magnetic resonance phase contrast cine imaging method according to claim 1, characterized in that: The flow velocity encoding includes several flow encoding directions. Based on the phase distribution modification and calculation performed on the third flow velocity phase map and the mask map, the human tissue information map to be solved is obtained based on the components of each flow encoding direction. The components of each flow encoding direction are merged to obtain the human tissue information map.

8. The magnetic resonance phase contrast cine imaging method according to any one of claims 1 to 7, characterized in that: The flow velocity encoding includes three flow encoding directions.

9. The magnetic resonance phase contrast cine imaging method according to claim 1, characterized in that: The step of obtaining the P tissue status codes comprises: The same spoke data based on the phase contrast sequence in different channels are merged to obtain a merged spoke, the low-frequency part of the merged spoke is extracted, and according to the acquisition time of the signal acquisition, a signal intensity sequence based on the acquisition time is obtained, the signal intensity sequence is filtered to obtain a filtered signal, and P tissue state codes are selected based on the filtered signal.

10. The magnetic resonance phase contrast cine imaging method according to claim 1, characterized in that: The step of obtaining the coil sensitivity information comprises: The coil sensitivity information of the magnetic resonance system is obtained based on a second reconstructed image reconstructed from a second k-space composed of a plurality of sub-k-spaces, wherein the second k-space is composed of a plurality of continuously sampled sub-k-spaces and the second k-space meets the full sampling condition.

11. The magnetic resonance phase contrast cine imaging method according to claim 10, characterized in that: A radial-2D and stacked star method is used to acquire signals of C channels from a target object, and the coil sensitivity information of the magnetic resonance system is obtained based on a second reconstructed image reconstructed based on a second k-space composed of multiple sub-k-spaces; wherein the second k-space is composed of a number of continuously sampled sub-k-spaces, and in the step where the second k-space meets the full sampling condition, the full sampling condition is that the number of points of each spoke is matrixsize*2, the number of spokes of the second k-space is greater than matrixsize*pi / 2, and matrixsize represents the matrix size of the first k-space.

12. The magnetic resonance phase contrast cine imaging method according to claim 10, characterized in that: A radial-3D method is used to acquire signals of C channels from a target object, and the coil sensitivity information of the magnetic resonance system is obtained by reconstructing a second k space composed of multiple sub-k spaces; wherein the second k space is composed of a number of continuously sampled sub-k spaces, and in the step where the second k space meets the full sampling condition, the full sampling condition is that the number of points of each spoke is matrixsize*2, and the number of spokes of the second k space is greater than matrixsize*matrixsize*pi*pi / 4, and matrixsize represents the matrix size of the first k space.

13. The magnetic resonance phase contrast cine imaging method according to claim 1, characterized in that: In the step of reconstructing based on the first k-space and coil sensitivity information using a compressed sensing method to obtain a first reconstructed image set of P*V first reconstructed sub-images based on tissue state coding and flow velocity coding information, non-uniform Fourier transform is used for data fidelity.

14. The magnetic resonance phase contrast cine imaging method according to claim 1, characterized in that: The step of reconstructing by using a compressed sensing method based on the first k-space and coil sensitivity information to obtain a first reconstructed image set of P*V first reconstructed sub-images based on tissue state coding and flow velocity coding information comprises: Based on a first operation item including the first k-space, a second operation item including a reconstructed image set of all tissue state codes corresponding to each flow velocity coding information, a third operation item including a reconstructed image set of all flow velocity coding information corresponding to each tissue state coding, and the coil sensitivity information, a compressed sensing method is used to reconstruct and obtain a first reconstructed image set of P*V first reconstructed sub-images based on tissue state coding and flow velocity coding information.

15. The magnetic resonance phase contrast cine imaging method according to claim 1, characterized in that: The step of providing a mask image, wherein the mask image is used to distinguish biological tissue from air, comprises: A mask image for distinguishing biological tissue from air is obtained by performing calculations based on a second reconstructed image obtained by reconstructing a second k-space composed of a plurality of sub-k-spaces and a preset threshold.

16. The magnetic resonance phase contrast cine imaging method according to claim 15, characterized in that: The step of providing a mask image, wherein the mask image is used to distinguish biological tissue from air comprises: The second reconstructed image reconstructed based on the second k-space is binarized based on a preset threshold value to obtain a mask image for distinguishing biological tissue from air.

17. A magnetic resonance system, characterized in that: include: Magnetic resonance controller; as well as A memory storing a program, the program comprising instructions, the magnetic resonance controller being connected to the memory and configured to execute the instructions, wherein the instructions, when executed by the magnetic resonance controller, cause the magnetic resonance system to perform the magnetic resonance phase contrast cine imaging method according to any one of claims 1 to 16.

18. A terminal device, characterized in that: The invention comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the magnetic resonance phase contrast cine imaging method according to any one of claims 1 to 16 is implemented.

19. A computer program product, characterized in that The invention comprises a program executed by at least one processor of a computing device, the program comprising instructions, wherein execution of the instructions causes the at least one processor to perform the magnetic resonance phase contrast cine imaging method according to any one of claims 1 to 16.

20. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to perform the magnetic resonance phase contrast cine imaging method according to any one of claims 1 to 16.

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